Magnus Lundberg Nordenvaad

Underwater communication link with iterative equalization

In this paper an acoustic underwater communication link is presented. The channel has severe inter symbol interference, which is dealt with by an iterative linear equalizer and a Turbo code. Experiments has been performed in the Baltic Sea using a 4QAM signal with bandwidth 4 kHz at a center frequency of 12 kHz. The raw data rate is 8 kbit/s but after reduction for error correction coding the net bit rate is 2.88 kbit/s. With a source level of 180 dB re. 1mu/Pa @ 1m and a single hydrophone receiver, a reliable communication is shown up to a distance of 60 km. Very important for a successful decoding is the initial synchronization, which also is discussed

Iterative Reception for Acoustic Underwater MIMO Communications

This paper presents a soft input/soft output linear equalizer for Alamouti encoded MIMO signals. The derived structure allows for simultaneous equalization of MIMO channels and decoding of Alamouti coded signals. The equalizer/decoder is here used within the turbo equalization framework to exploit the complex and rich characteristics of the acoustic underwater channel. Such schemes can operate ate very low signal-to-noise ratio (SNR) levels enabling high transmission rates over long distances. We investigate the viability of the technique by using a simulation example and by studying its behavior for a real scenario, using data collected in the Baltic Sea

Enhanced channel estimation and efficient symbol detection in MIMO underwater acoustic communications

Effective training sequences and reliable channel estimation algorithms are essential for enhancing the performance of multi-input multi-output (MIMO) underwater acoustic communications (UAC). Also, effective interference cancellation schemes are crucial for reliable symbol detection. In this paper, the problem of designing MIMO training sequences is considered. Moreover, we present a sparse learning via iterative minimization (SLIM) algorithm for enhanced channel estimation and reduced computational complexity. Furthermore, RELAX-BLAST, a linear minimum mean-squared error based symbol detection scheme, is implemented efficiently by exploiting the conjugate gradient method and diagonalization properties of circulant matrices. The proposed MIMO UAC techniques are evaluated using both simulated and experimental examples.

On Bayesian Channel Estimation and FFT-Based Symbol Detection in MIMO Underwater Acoustic Communications

Reliable channel estimation and effective interference cancellation are essential for enhancing the performance of multiple-input-multiple-output (MIMO) underwater acoustic communication (UAC) systems. In this paper, an efficient user-parameter-free Bayesian approach, referred to as sparse learning via iterative minimization (SLIM), is presented. SLIM provides good channel estimation performance along with reduced computational complexity compared to iterative adaptive approach (IAA). Moreover, RELAX-BLAST, which is a linear minimum mean-squared error (MMSE)-based symbol detection scheme, is implemented efficiently by making use of the conjugate gradient (CG) method and diagonalization properties of circulant matrices. The proposed algorithm requires only simple fast Fourier transform (FFT) operations and facilitates parallel implementations. These MIMO UAC techniques are evaluated using both simulated and in-water experimental examples. The 2008 Surface Processes and Acoustic Communications Experiment (SPACE08) experimental results show that the proposed MIMO UAC schemes can enjoy almost error-free performance even under severe ocean environments.

A Bayesian Analysis of Unreplicated Two-Level Factorials Using Effects Sparsity, Hierarchy, and Heredity

ABSTRACT This article proposes a Bayesian procedure to calculate posterior probabilities of active effects for unreplicated two-level factorials. The results from a literature survey are used to specify individual prior probabilities for the activity of effects and the posterior probabilities are then calculated in a three-step procedure where the principles of effects sparsity, hierarchy, and heredity are successively considered. We illustrate our approach by reanalyzing experiments found in the literature.

On preconditioned conjugate gradient method for time-varying OFDM channel equalization

We consider using the conjugate gradient (CG) algorithm to equalize a time-varying channel in an orthogonal frequency division multiplexing (OFDM) system. Preconditioning technique to accelerate the convergence of the CG algorithm is discussed, where we show that when the Doppler spread becomes higher, the commonly used diagonal preconditioner, despite its simpleness, can perform even worse than without preconditioner. In such a case, a preconditioner with a more complex structure is proposed.

Extended Kalman filter for vehicle tracking using road surface vibration measurements

This paper addresses a novel method for vehicle tracking using an extended Kalman filter and measurements of road surface vibrations from a single accelerometer. First, a measurement model for vibrations caused by vehicular road traffic is developed. Then the identifiability of the involved parameters is analyzed. Finally, the measurement model is combined with a constant speed motion model and the Kalman filter is derived. Simulation and measurement results indicate that the approach is feasible and show where further development is needed.

Multi-input multi-output underwater communications over sparse and frequency modulated acoustic channels

This paper addresses multi-input multi-output (MIMO) communications over sparse acoustic channels suffering from frequency modulations. An extension of the recently introduced SLIM algorithm, which stands for sparse learning via iterative minimization, is presented to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). The sparseness is exploited through a hierarchical Bayesian model, and because GoSLIM is user parameter free, it is easy to use in practical applications. Moreover this paper considers channel equalization and symbol detection for various MIMO transmission schemes, including both space-time block coding and spatial multiplexing, under the challenging channel conditions. The effectiveness of the proposed approaches is demonstrated using in-water experimental measurements recently acquired during WHOI09 and ACOMM10 experiments.

Joint Vehicle Trajectory and Model Parameter Estimation Using Road Side Sensors

This paper shows how a particle smoother-based system identification method can be applied for estimating the trajectory of road vehicles. As sensors, a combination of an accelerometer measuring the road surface vibrations and a magnetometer measuring magnetic disturbances mounted on the side of the road are considered. First, sensor models describing the measurements of the two sensors are introduced. It is shown that these depend on unknown, static parameters that have to be considered in the estimation. Second, the sensor models are combined with a two-dimensional constant velocity motion model. Third, the system identification algorithm is introduced which iteratively runs a Rao-Blackwellized particle smoother to estimate the vehicle trajectory followed by an expectation-maximization step to estimate the parameters. Finally, the method is applied to both simulation and measurement data. It is found that the method works well in general and some issues when real data is considered are identified as future work.

Equalization for multi-scale multi-lag OFDM channels

We consider an orthogonal frequency-division multiplexing (OFDM) transmission scheme over wideband underwater acoustic channels, where the propagation paths can experience distinct Doppler effects (manifested in signal scales) and time of arrivals (manifested in lags). We capture such an effect in this paper with a multi-scale multi-lag (MSML) model, and show that the resulting frequency-domain MSML-OFDM channel is subject to inter-carrier interference (ICI), whose amount differs per subcarrier. The corresponding channel matrix can still be approximated as highly sparse, but lacks a specific structure that can optimally be exploited by those low-complexity equalizers proposed for narrowband channels. In this paper, we propose to use the conjugate gradient (CG) algorithm to equalize the channel iteratively. The suitability of the preconditioning technique, that often accompanies the CG to accelerate the convergence, is discussed for the MSML-OFDM channel. We show that in order for the preconditioner to function properly, optimal resampling is indispensible.